We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network (GAN) to transform diffraction-limited input images into super-resolved ones. Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. Read More

Authors:

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.

We have developed a computational method based on polyploid phasing of long sequence reads to resolve collapsed regions of segmental duplications within genome assemblies. Segmental Duplication Assembler (SDA; https://github.com/mvollger/SDA ) constructs graphs in which paralogous sequence variants define the nodes and long-read sequences provide attraction and repulsion edges, enabling the partition and assembly of long reads corresponding to distinct paralogs. Read More

We describe droplet-assisted RNA targeting by single-cell sequencing (DART-seq), a versatile technology that enables multiplexed amplicon sequencing and transcriptome profiling in single cells. We applied DART-seq to simultaneously characterize the non-A-tailed transcripts of a segmented dsRNA virus and the transcriptome of the infected cell. In addition, we used DART-seq to simultaneously determine the natively paired, variable region heavy and light chain amplicons and the transcriptome of B lymphocytes. Read More

Determining the structure and composition of macromolecular assemblies is a major challenge in biology. Here we describe ultrastructure expansion microscopy (U-ExM), an extension of expansion microscopy that allows the visualization of preserved ultrastructures by optical microscopy. This method allows for near-native expansion of diverse structures in vitro and in cells; when combined with super-resolution microscopy, it unveiled details of ultrastructural organization, such as centriolar chirality, that could otherwise be observed only by electron microscopy. Read More

U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples. Read More

It is extremely challenging to quantitate lumenal Ca in acidic Ca stores of the cell because all Ca indicators are pH sensitive, and Ca transport is coupled to pH in acidic organelles. We have developed a fluorescent DNA-based reporter, CalipHluor, that is targetable to specific organelles. By ratiometrically reporting lumenal pH and Ca simultaneously, CalipHluor functions as a pH-correctable Ca reporter. Read More

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. Read More

Authors:

Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.

A central and critical structure in tuberculosis, the mycobacterial granuloma consists of highly organized immune cells, including macrophages that drive granuloma formation through a characteristic epithelioid transformation. Difficulties in imaging within intact animals and caveats associated with in vitro assembly models have severely limited the study and experimental manipulation of mature granulomas. Here we describe a new ex vivo culture technique, wherein mature, fully organized zebrafish granulomas are microdissected and maintained in three-dimensional (3D) culture. Read More

Whole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measured activity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. Read More

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). Read More

Scarless genome editing in human pluripotent stem cells (hPSCs) represents a goal for both precise research applications and clinical translation of hPSC-derived therapies. Here we established a versatile and efficient method that combines CRISPR-Cas9-mediated homologous recombination with positive-negative selection of edited clones to generate scarless genetic changes in hPSCs. Read More

Single-particle electron cryomicroscopy (cryo-EM) involves estimating a set of parameters for each particle image and reconstructing a 3D density map; robust algorithms with accurate parameter estimation are essential for high resolution and automation. We introduce a particle-filter algorithm for cryo-EM, which provides high-dimensional parameter estimation through a posterior probability density function (PDF) of the parameters given in the model and the experimental image. The framework uses a set of random support points to represent such a PDF and assigns weighting coefficients not only among the parameters of each particle but also among different particles. Read More

An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present a statistical algorithm called CellDMC ( https://github.com/sjczheng/EpiDISH ), which can identify differentially methylated positions and the specific cell type(s) driving the differential methylation. Read More

In the version of Supplementary Fig. 1 originally published with this paper, some images in panel e were accidental duplicates of images in panel b. This error has been corrected in the online integrated supplementary information and in the Supplementary Information PDF. Read More

Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. Read More

A key unmet challenge in interpreting omics experiments is inferring biological meaning in the context of public functional genomics data. We developed a computational framework, Your Evidence Tailored Integration (YETI; http://yeti.princeton. Read More

Authors:

Laboratory of Muscle Stem Cells and Gene Regulation, National Institute of Arthritis and Musculoskeletal and Skin Disease, Bethesda, MD, USA.

Methods for the systematic study of subcellular RNA localization are limited, and their development has lagged behind that of proteomic tools. We combined APEX2-mediated proximity biotinylation of proteins with photoactivatable ribonucleoside-enhanced crosslinking to simultaneously profile the proteome and the transcriptome bound by RNA-binding proteins in any given subcellular compartment. Our approach is fractionation independent and allows study of the localization of RNA processing intermediates, as well as the identification of regulatory RNA cis-acting elements occupied by proteins, in a cellular-compartment-specific manner. Read More

Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man. Read More

Authors:

Section on High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA.

Structured illumination microscopy (SIM) allows rapid, super-resolution (SR) imaging in live specimens. We review recent technical advances in SR-SIM, with emphasis on imaging speed, resolution, and depth. Since its introduction decades ago, the technique has grown to offer myriad implementations, each with its own strengths and weaknesses. Read More

Acoustic tweezers are a versatile set of tools that use sound waves to manipulate bioparticles ranging from nanometer-sized extracellular vesicles to millimeter-sized multicellular organisms. Over the past several decades, the capabilities of acoustic tweezers have expanded from simplistic particle trapping to precise rotation and translation of cells and organisms in three dimensions. Recent advances have led to reconfigured acoustic tweezers that are capable of separating, enriching, and patterning bioparticles in complex solutions. Read More

The original version of this paper contained an incorrect primer sequence. In the Methods subsection "Rampage libraries," the text for modification 3 stated that the reverse primer used for library indexing was 5'-CAAGCAGAAGACGGCATACGAGATXXXXXXXXGTGACTGGAGT-3'. The correct sequence of the oligonucleotide used is 5'-CAAGCAGAAGACGGCATACGAGATXXXXXXXXGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3'. Read More

We developed a dual-adeno-associated-virus expression system that enables strong and sparse labeling of individual neurons with cell-type and projection specificity. We demonstrated its utility for whole-brain reconstruction of midbrain dopamine neurons and striatum-projecting cortical neurons. We further extended the labeling method for rapid reconstruction in cleared thick brain sections and simultaneous dual-color labeling. Read More

Authors:

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

The accurate quantification of microbial growth dynamics for species without complete genome sequences is biologically important, but computationally challenging in metagenomics. Here we present dynamic estimator of microbial communities (DEMIC; https://sourceforge.net/projects/demic/ ), a multi-sample algorithm based on contigs and coverage values, to infer the relative distances of contigs from the replication origin and to accurately compare bacterial growth rates between samples. Read More

The version of this paper originally published contained errors in reference citations: in the first paragraph of the Results section, the text "This extent of optical clarity probably results from the absence of skull above the brain. In our specimens, Nissl-stained coronal sections through the head showed that the skull surrounds the brain only laterally and ventrally" should have read "This extent of optical clarity probably results from the absence of skull above the brain. In our specimens, Nissl-stained coronal sections through the head showed that the skull surrounds the brain only laterally and ventrally. Read More